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arXiv Machine Learning By Yi Zhu, Su Chen, Xiaojun Li

Seismic Site Response Prediction from Sparse Observations Using Finite-Element-Pretrained Latent Dynamics

Read the original on arXiv Machine Learning →

The paper introduces FLARE‑T, a Transfer‑Enabled Forced Latent Autoencoder for Response Equations, which learns low‑dimensional latent dynamics from dense finite‑element simulations and calibrates them with sparse field observations. By mapping simulated sensor responses into a learned coordinate system, FLARE‑T improves multi‑depth acceleration predictions and pseudo‑acceleration spectra, reducing errors across various sensor locations and motion intensities. Evaluation on a layered‑soil centrifuge test and the Lotung field array demonstrates that FLARE‑T achieves comparable accuracy with different source models, indicating less reliance on precise prior calibration.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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